One of the most common censoring methods is right-handed incremental censoring. In this censoring method, $n$ units are included in the experiment and at the time of failure of each unit, a number of remaining units are randomly removed from the experiment. This continues until $m$ unit failure times are recorded for a predetermined value such as $m$ and then the experiment is terminated. The problem of determining the optimal censoring design in the type-two incremental censoring model is a problem that has been studied so far based on different criteria. Another problem in the type-two incremental censoring model is the selection of the sample size at the beginning of the experiment, i.e. $n$. In this paper, assuming a Pareto distribution for the data under study and the Fisher information criterion, the determination of the optimal sample size, i.e. $n_{opt}$, as well as the optimal censoring design are studied. Finally, in order to evaluate the obtained results, numerical calculations and a real example are presented using the $R$ software.